A Conservative Prediction Model of Power System Transient Stability

Liu, Juhua; Wang, Xu; Sun, Hao; Zhao, ongyan; Cheng, Lin; Ke, Xianbo; Sun, Xiuting; Ping, Wei

doi:10.1109/pesgm.2018.8586171

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…In [61], a different time series forecasting algorithm, using SVM, was proposed, which utilized synchronized phasor data, to provide fast transient stability swings prediction, for the use of emergency control. In [62], a conservative prediction model, for power system transient stability, was suggested, targeting at enhancing accuracy, for predicting the unstable cases. The model was recognized as an ensemble learning model, using multiple SVMs as sub-learning machines.…”

Section: Kernel Equationmentioning

confidence: 99%

Prediction of Probabilistic Transient Stability Using Support Vector Machine

Shahzad

2022

TEEE

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Transient stability assessment is an integral part of dynamic security assessment of power systems. Traditional methods of transient stability assessment, such as time domain simulation approach and direct methods, are appropriate for offline studies and thus, cannot be applied for online transient stability prediction, which is a major requirement in modern power systems. This motivated the requirement to apply an artificial intelligence-based approach. In this regard, supervised machine learning is beneficial for predicting transient stability status, in the presence of uncertainties. Therefore, this paper examines the application of a binary support vector machine-based supervised machine learning, for predicting the transient stability status of a power system, considering uncertainties of various factors, such as load, faulted line, fault type, fault location and fault clearing time. The support vector machine is trained using a Gaussian Radial Basis function kernel and its hyperparameters are optimized using Bayesian optimization. Results obtained for the IEEE 14-bus test system demonstrated that the proposed method offers a fast technique for probabilistic transient stability status prediction, with an excellent accuracy. DIgSILENT PowerFactory and MATLAB was utilized for transient stability time-domain simulations (for obtaining training data for support vector machine) and for applying support vector machine, respectively.

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Section: Kernel Equationmentioning

confidence: 99%

Prediction of Probabilistic Transient Stability Using Support Vector Machine

Shahzad

2022

TEEE

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“…It should also be noted that in actual power systems, the typical sampling frequency for the fundamental phasor of the trajectory variables is 10 ms for the PMUs in the wide area measurement system (WAMS). The transient instability criterion is set as, if the maximum rotor angle difference between any pair of generators exceeds 180º at the end of the transient simulation (such as 5s), the case will be recognized as unstable [36]. Figure 4 shows the bus voltage amplitude curves of a typical transiently stable sample (in blue) and an unstable sample (in red), with the four series of sampled data.…”

Section: Three-time Stages Related To Transient Stabilitymentioning

confidence: 99%

“…In this paper, the SVM model used is libsvm2.0 [38], and the optimal penalty coefficients C and RBF kernel parameters of the SVM classifiers are obtained by grid search [36] and 5-fold cross-validation using the training samples. Then, the entire training samples and the optimal SVM parameters are used to retrain the SVM classifiers.…”

Section: Sample Generation and Data Series Analysis With Traditional mentioning

confidence: 99%

An Improved Power System Transient Stability Prediction Model Based on mRMR Feature Selection and WTA Ensemble Learning

Liu

Sun

et al. 2020

Applied Sciences

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Fast online transient stability assessment (TSA) is very important to maintain the stable operation of power systems. However, the existing transient stability assessment methods suffer the drawbacks of unsatisfactory prediction accuracy, difficult applicability, or a heavy computational burden. In light of this, an improved high accuracy power system transient stability prediction model is proposed, based on min-redundancy and max-relevance (mRMR) feature selection and winner take all (WTA) ensemble learning. Firstly, the contributions of four different series of raw sampled data from all of the three-time stages, namely the pre-fault, during-fault and post-fault, to transient stability are compared. The new feature of generator electromagnetic power is introduced and compared with three conventional types of input features, through a support vector machine (SVM) classifier. Furthermore, the two types of most contributive input features are obtained by the mRMR feature selection method. Finally, the prediction results of the electromagnetic power of generators and the voltage amplitude of buses are combined using the WTA ensemble learning method, and an improved transient stability prediction model with higher accuracy for unstable samples is obtained, whose overall prediction accuracy would not decrease either. The real-time data collected by wide area monitoring systems (WAMS) can be fed into this model for fast online transient stability prediction; the results can also provide a basis for the future emergency control decision-making of power systems.

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